diff --git a/src/transformers/models/mbart/modeling_mbart.py b/src/transformers/models/mbart/modeling_mbart.py
index b035ef7b10d3e3..276f94aebdbb9e 100755
--- a/src/transformers/models/mbart/modeling_mbart.py
+++ b/src/transformers/models/mbart/modeling_mbart.py
@@ -1184,6 +1184,11 @@ def get_encoder(self):
     def get_decoder(self):
         return self.decoder
 
+    def _tie_weights(self):
+        if self.config.tie_word_embeddings:
+            self._tie_or_clone_weights(self.encoder.embed_tokens, self.get_input_embeddings())
+            self._tie_or_clone_weights(self.decoder.embed_tokens, self.get_input_embeddings())
+
     @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,
diff --git a/tests/models/mbart/test_modeling_mbart.py b/tests/models/mbart/test_modeling_mbart.py
index db5b554e82d43a..deaa8b5dafe676 100644
--- a/tests/models/mbart/test_modeling_mbart.py
+++ b/tests/models/mbart/test_modeling_mbart.py
@@ -327,6 +327,43 @@ def test_generate_fp16(self):
         model.generate(input_ids, attention_mask=attention_mask)
         model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
 
+    def test_ensure_weights_are_shared(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
+
+        config.tie_word_embeddings = True
+        model = MBartForConditionalGeneration(config)
+
+        # MBart shares four weights.
+        # Not an issue to not have these correctly tied for torch.load, but it is an issue for safetensors.
+        self.assertEqual(
+            len(
+                {
+                    model.get_output_embeddings().weight.data_ptr(),
+                    model.get_input_embeddings().weight.data_ptr(),
+                    model.base_model.decoder.embed_tokens.weight.data_ptr(),
+                    model.base_model.encoder.embed_tokens.weight.data_ptr(),
+                }
+            ),
+            1,
+        )
+
+        config.tie_word_embeddings = False
+        model = MBartForConditionalGeneration(config)
+
+        # MBart shares four weights.
+        # Not an issue to not have these correctly tied for torch.load, but it is an issue for safetensors.
+        self.assertEqual(
+            len(
+                {
+                    model.get_output_embeddings().weight.data_ptr(),
+                    model.get_input_embeddings().weight.data_ptr(),
+                    model.base_model.decoder.embed_tokens.weight.data_ptr(),
+                    model.base_model.encoder.embed_tokens.weight.data_ptr(),
+                }
+            ),
+            2,
+        )
+
 
 def assert_tensors_close(a, b, atol=1e-12, prefix=""):
     """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""